Which is more popular, Dify or LangGraph?

Dify has more GitHub stars (157,652 vs 42,550).

Which is more actively developed, Dify or LangGraph?

Dify had more commits in the last 90 days (2,316 vs 137).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

Dify is growing faster: +3,659 GitHub stars in the last 30 days vs +2,374 for LangGraph.
Pick Dify for: production-ready platform for agentic workflow development. Pick LangGraph for: build resilient language agents as graphs.

From GitHub data refreshed daily.

Production-ready platform for agentic workflow development.

LangGraphopen-source

Build resilient language agents as graphs.

+Durable execution ensures agents automatically resume from exactly where they left off after failures or interruptions
+Comprehensive memory system with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions
+Seamless human-in-the-loop capabilities allow for inspection and modification of agent state at any point during execution

-Low-level framework requires more technical expertise and setup compared to high-level agent builders
-Graph-based agent design paradigm may have a steeper learning curve for developers new to agent orchestration
-Production deployment complexity may be overkill for simple chatbot or single-turn use cases

•Long-running autonomous agents that need to persist through system failures and operate over days or weeks
•Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
•Stateful agents that must maintain context and memory across multiple sessions and interactions

Which is more popular, Dify or LangGraph?: Dify has more GitHub stars (157,652 vs 42,550).
Which is more actively developed, Dify or LangGraph?: Dify had more commits in the last 90 days (2,316 vs 137).
Should I use Dify or LangGraph?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.